Information Theory, Inference & Learning Algorithms - Info and Reading Options
By David J.C. MacKay ( April 22, 1967 - 14 April 2016 )
“Information Theory, Inference & Learning Algorithms” Metadata:
- Title: ➤ Information Theory, Inference & Learning Algorithms
- Author: David J.C. MacKay
“Information Theory, Inference & Learning Algorithms” Subjects and Themes:
- Subjects: ➤ Information theory - Inference - Machine Learning - Bayesian - Aprendizado computacional - Information, Théorie de l' - Inferenz - Statistische analyse - Toepassingen - Maschinelles Lernen - Informationstheorie - Teoria da informação - Informatietheorie - Algoritmen - Algorithms - Teoria da informacao - Information, Theorie de l' - Inferenz <künstliche intelligenz> - Inferenz (künstliche intelligenz) - Q360 .m23 2003 - 003/.54 - Dat 708f - Qh 210 - Sk 880 - St 130 - St 300
Edition Identifiers:
- The Open Library ID: OL8325677W
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"Information Theory, Inference & Learning Algorithms" Description:
The Open Library:
Book Jacket: > This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. Publisher Description: > This textbook offers comprehensive coverage of Shannon's theory of information as well as the theory of neural networks and probabilistic data modelling. It includes explanations of Shannon's important source encoding theorem and noisy channel theorem as well as descriptions of practical data compression systems. Many examples and exercises make the book ideal for students to use as a class textbook, or as a resource for researchers who need to work with neural networks or state-of-the-art error-correcting codes.
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